000			08044nam a22002537a 4500
008			210223b2016 a|||f mb|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a005
100	0		_aMohamed Seif Eldin Mohamed Abdelmoneim Mohamed
245	1		_aInterference Management In Spectrally and Energy Efficient Wireless Networks / _cMohamed Seif Eldin Mohamed Abdelmoneim Mohamed
260			_c2016
300			_a96 p. _bill. _c21 cm.
500			_3Supervisor: Mohamed Nafie
502			_aThesis (M.A.)—Nile University, Egypt, 2016 .
504			_a"Includes bibliographical references"
505	0		_aContents: 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Achievable Degrees of Freedom of the K-user MISO Broadcast Channel with Alternating CSIT via Interference Creation-Resurrection . . . . . . 4 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Proposed Interference Creation-Resurrection Scheme . . . . . . . . 11 2.3.1 Phase 1: Interference Creation . . . . . . . . . . . . . . 12 2.3.2 Phase 2: Interference Resurrection . . . . . . . . . . . . 13 2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3. Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms . . . . . . . . . . . . . . . . . . . . . . 23 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 x 3.2.1 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . 25 3.2.2 Binary Consensus Algorithm . . . . . . . . . . . . . . . . . 27 3.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4 Proposed Sensing Scheme . . . . . . . . . . . . . . . . . . . . . . . 33 3.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4. Cooperative D2D Communication in Downlink Cellular Networks with Energy Harvesting Constraints . . . . . . . . . . . . . . . . . . . . . . . 42 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.2.1 Direct Transmission Scheme . . . . . . . . . . . . . . . . . . 47 4.2.2 Cooperative Transmission Scheme . . . . . . . . . . . . . . 47 4.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5. Sparse Signal Processing Concepts for Efficient 5G System Design . . . . 59 5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2 Enabling 5G Technical Concepts . . . . . . . . . . . . . . . . . . . 60 5.3 Joint Activity and Data Detection for Machine to Machine Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.5 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.6 Proposed Solutions for Activity and Data Detection . . . . . . . . 64 5.6.1 Generalized Likelihood Ratio Test . . . . . . . . . . . . . . 64 5.6.2 MMSE Activity Recovery . . . . . . . . . . . . . . . . . . . 65 6. Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.2 Future Work: Joint Activity and Data Detection for Machine to Machine Communication . . . . . . . . . . . . . . . . . . . . . . . . 68 6.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Proposed Solutions for Activity and Data Detection . . . . 71 6.4.2 Generalized Likelihood Ratio Test . . . . . . . . . . . . . . 71 6.4.3 MMSE Activity Recovery . . . . . . . . . . . . . . . . . . . 71 Bibliography . . . . . . . . . . . . . . .
520	3		_aAbstract: In this thesis, we explore different trends in the design of wireless networks. The first work of this thesis, we investigate on the interference management problem with limited channel state information in wireless network specifically at the transmitter(s). Channel state information at the transmitter affects the degrees of freedom of the wireless networks. In this paper, we analyze the DoF for the K-user multiple-input single-output (MISO) broadcast channel (BC) with synergistic alternating channel state information at the transmitter (CSIT). Specifically, the CSIT of each user alternates between three states, namely, perfect CSIT (P), delayed CSIT (D) and no CSIT (N) among different time slots. For the K-user MISO BC, we show that the total achievable degrees of freedom (DoF) are given by K2 2K−1 through utilizing the synergistic benefits of CSIT patterns. We compare the achievable DoF with results reported previously in the literature in the case of delayed CSIT and hybrid CSIT models. Secondly, Compressive Sensing (CS) is utilized in Cognitive Radio Networks (CRNs) to exploit the sparse nature of the occupation of the primary users. Also, distributed spectrum sensing has been proposed to tackle the wireless channel problems, like node or link failures, rather than the common “centralized approach” for spectrum sensing. In this work, we propose a distributed spectrum sensing framework based on consensus algorithms where SU nodes exchange their binary decisions to take global decisions without a fusion center to coordinate the sensing process. Each SU will share its decision with its neighbors, and at every new iteration each SU will take a new decision based on its current decision and the decisions it receives from its neighbors; in the next iteration, each SU will share its new decision with its neighbors. We show via simulations that the detection performance can tend to the performance of majority-rule Fusion Center based CRNs. As a solution for the spectrum shrinkage, Device-to-Device (D2D) communications have been highlighted as one of the promising solutions to enhance spectrum utilization of LTE-Advanced networks. In this work, we consider a D2D transmitter cooperating with a cellular network by acting as a relay serve one of the cellular user equipments. We consider the case in which the D2D transmitter is equipped with an energy harvesting capability. We investigate the tradeoff between the amount of energy used for relaying and the energy used for decoding the cellular user data. We formulate an optimization problem to maximize the cellular user rate subject to a minimum rate requirement constraint for the D2D link. Finally, we show via numerical simulations the benefits of our cooperation-based system as compared to the non-cooperative scenario. It is inevitable that, 4G will not be able to the user demands that is the data traffic is growing exponentially. Due to the advent of Compressive Sensing (CS), methods that can optimally exploit sparsity in signals that will be the key enabler in the design of 5G systems. We give a glimpse on the future design aspects in 5G communications systems. Besides that, a new type of communication system called Machine-to- Machine (M2M) communications that will be involved with a salient portion of the 5G data traffic is highlighted. We study the problem of multi-user detection (MUD) in M2M communications utilizing the tool of CS, also, proposing different recovery techniques with the aid of multiple antennas techniques.
546			_aText in English, abstracts in English.
650		4	_aWireless Technologies _9327
655		7	_2NULIB _aDissertation, Academic _9187
690			_aWireless Technologies _9327
942			_2ddc _cTH
999			_c8959 _d8959